This work aims to examine one of the cornerstone problems of Musical
Instrument Retrieval (MIR), in particular, instrument classification. IRMAS
(Instrument recognition in Musical Audio Signals) data set is chosen for this
purpose. The data includes musical clips recorded from various sources in the
last century, thus having a wide variety of audio quality. We have presented a
very concise summary of past work in this domain. Having implemented various
supervised learning algorithms for this classification task, SVM classifier has
outperformed the other state-of-the-art models with an accuracy of 79%. We also
implemented Unsupervised techniques out of which Hierarchical Clustering has
performed well.Comment: Appeared in Proceedings of SPIN 202